From e32df3696415c8ca07fbb440768f8a07201097e4 Mon Sep 17 00:00:00 2001 From: eric Date: Tue, 14 Jan 2025 17:00:07 +0100 Subject: [PATCH] update --- doc_network/bso.bib | 14 +- doc_network/bso.md | 22 +- doc_network/howto.pdf | Bin 298764 -> 0 bytes doc_network/howto.tex | 600 ------------------------------- doc_network/mapping_at_scale.pdf | Bin 284380 -> 286813 bytes doc_network/mapping_at_scale.tex | 57 ++- doc_network/out.docx | Bin 69240 -> 70173 bytes doc_network/out.enriched.json | 2 +- doc_network/out.epub | Bin 66476 -> 67345 bytes doc_network/out.html | 18 +- doc_network/out.latex | 57 ++- doc_network/out.odt | Bin 66978 -> 67633 bytes doc_network/out.pdf | Bin 284380 -> 286813 bytes 13 files changed, 142 insertions(+), 628 deletions(-) delete mode 100644 doc_network/howto.pdf delete mode 100644 doc_network/howto.tex diff --git a/doc_network/bso.bib b/doc_network/bso.bib index 6ec2b98..b216517 100644 --- a/doc_network/bso.bib +++ b/doc_network/bso.bib @@ -134,4 +134,16 @@ @article{Blondel_2008 title = {Fast unfolding of communities in large networks}, journal = {Journal of Statistical Mechanics: Theory and Experiment}, abstract = {We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.} -} \ No newline at end of file +} + +@unpublished{bassinet:hal-04121339, + TITLE = {{Large-scale Machine-Learning analysis of scientific PDF for monitoring the production and the openness of research data and software in France}}, + AUTHOR = {Bassinet, Aricia and Bracco, Laetitia and L'H{\^o}te, Anne and Jeangirard, Eric and Lopez, Patrice and Romary, Laurent}, + URL = {https://hal.science/hal-04121339}, + NOTE = {working paper or preprint}, + YEAR = {2023}, + KEYWORDS = {research data ; research software ; open access ; open science ; scientometrics}, + PDF = {https://hal.science/hal-04121339v3/file/BSO3_preprint_20230625.pdf}, + HAL_ID = {hal-04121339}, + HAL_VERSION = {v3}, +} diff --git a/doc_network/bso.md b/doc_network/bso.md index 6474e4f..b76e40d 100644 --- a/doc_network/bso.md +++ b/doc_network/bso.md @@ -36,7 +36,7 @@ Analysing and mapping scientific communities provides an insight into the struct These maps are generally deduced from data in bibliographic databases (open or proprietary), based on co-publication or citation information. In the case of co-publications, two entities (authors, for example) will be linked if they have collaborated (co-published) on a piece of research. These links are then symmetrical. In the case of citation links, two authors will be linked if one cites the research work of another, in the list of references. This is a directed link, as one author may cite another without this being reciprocal. A lot of recent work uses this second approach, for example by trying to calculate composite indicators of novelty (or innovation) based on citation links. The quality and completeness of the bibliographic metadata used are, of course, essential if we are to produce a relevant map. Today, the quality of open citation data still needs to be improved, cf [@alperin2024analysissuitabilityopenalexbibliometric]. -On the other hand, it is possible to obtain quality metadata on publications (and therefore links to co-publications). For example, the French Open Science Monitor (BSO) has compiled a corpus of French publications with good coverage cf [@10.1162/qss_a_00179]. This corpus is exposed in the French research portal scanR [@jeangirard:hal-04813230]. This is a corpus containing about 4 millions publications in all disciplines. These publications have been enriched with disambuation persistent identifier (PID) on authors, affiliations and topics. For authors and affiliations, French-specific PID have been used (idref for authors and RNSR for laboratories) because they have the best coverage, even if not perfect. For topics, wikidata identifiers has been used cf [@foppiano2020entity]. Other enrichments, like software detection are also present, and thus usable as entities to map. +On the other hand, it is possible to obtain quality metadata on publications (and therefore links to co-publications). For example, the French Open Science Monitor (BSO) has compiled a corpus of French publications with good coverage cf [@10.1162/qss_a_00179]. This corpus is exposed in the French research portal scanR [@jeangirard:hal-04813230]. This is a corpus containing about 4 millions publications in all disciplines. These publications have been enriched with disambuation persistent identifier (PID) on authors, affiliations and topics. ## 1.1 Previous limits of the scanR application @@ -58,7 +58,19 @@ One of the added values of mapping with a network view is to show the interactio Thus, from a given corpus, however large, we seek to extract the pairs of entities with the strongest interactions, for example the most co-signatures per pair of authors. From this list of pairs, we can naturally find the nodes of the graph and deduce a new graph. If the graph has several independent components, i.e. several unconnected sub-graphs, we can decide to keep only the main component(s). -## 2.2 Elasticsearch implementation +## 2.2 Publication metadata enrichment to produce different mapping + +Each publication in the scanR corpus goes through a systematic enrichment pipeline, including author and affiliation disambiguation, full-text parsing, topic detection. + +For authors, the French-specific persistent identifier (PID) [https://www.idref.fr](https://www.idref.fr) is used. Its coverage, even if not perfect, for French affiliated authors is strong thanks to the deep linking between idref and the PhD thesis registration in France. Specific heuristics have been implemented to disambiguate names and link them to idref. + +For affiliations, again French specific PID are used, especially SIRENE and RNSR. A specific module based on Elasticsearch [https://github.com/dataesr/affiliation-matcher](https://github.com/dataesr/affiliation-matcher) has been implemented to automatically link pblications to those PIDs [@lhote_using_2021]. + +For topics, wikidata identifiers has been used using the entity-fishing module [https://github.com/kermitt2/entity-fishing](https://github.com/kermitt2/entity-fishing) cf [@foppiano2020entity]. + +Other enrichments, like software detection are also present. These are based on software mentions detections using GROBID and Softcite at scale on the French corpus [@bassinet:hal-04121339]. + +## 2.3 Elasticsearch implementation To identify the strongest links, it would be too costly to go through the entire corpus. We have pre-calculated the links at the level of each publication. So, if a publication is linked to 3 themes, T1, T2 and T3, a pre-calculated field, at publication level, contains all T1-T2, T1-T3 and T2-T3 pairs. This co_topics field represents the co-appearance links within the publication. We then use elasticsearch's aggregation functionality to list the most present links, very efficiently. @@ -99,7 +111,7 @@ In practice, a PID is also stored (the wikidata for topics, for example) to disa }, ``` -## 2.3 Network creation +## 2.4 Network creation The network creation process involves several key steps: transforming Elasticsearch results into a graph using Graphology, filtering the network to focus on the most interesting nodes, applying spatialization algorithms for visualization, and detecting communities within the network. Below, we detail each of these steps. @@ -122,7 +134,9 @@ slowDown: 1 + Math.log(order) In graph theory, a community corresponds to a set of nodes in a graph that are strongly interconnected with each other, while being less connected with nodes outside this community. Communities can be identified in order to understand the underlying structure and patterns of the graph, as well as to analyze the relationships and interactions between the entities that make it up. To identify and visualize communities within the network, we apply the Louvain algorithm using Graphology. This algorithm works by optimizing a modularity measure that evaluates the strength of communities in a graph [@Blondel_2008]. More precisely, Louvain seeks to maximize modularity by progressively moving the nodes of a graph into different communities, in an iterative fashion. At each stage, he merges neighboring communities if this leads to an improvement in the overall modularity of the graph. This iterative process continues until no further moves can increase modularity. This step helps in revealing the underlying structure and communities within the scientific network, providing valuable insights into the interactions and collaborations within the bibliometric data. -## 2.4 VOSviewer implementation +The `graphology-communities-louvain` node module is being used. This way, each step (like spatizalization, community-detection) are implemented modularly. A benchmark, in our use case, of the Louvain and the Leiden algorithms would be desirable. The graphology library started a while ago working on an implementation of the leiden algorithm (see [https://github.com/graphology/graphology/tree/master/src/communities-leiden](https://github.com/graphology/graphology/tree/master/src/communities-leiden)) but that remains to be implemented. + +## 2.5 VOSviewer implementation To display the network within our application, we use the open source VOSviewer online tool for network visualization [https://github.com/neesjanvaneck/VOSviewer-Online](https://github.com/neesjanvaneck/VOSviewer-Online). It is based on the VOSviewer software which is very popular for network analysis in bibliometric studies [@DBLP:journals/corr/abs-1006-1032]. diff --git a/doc_network/howto.pdf b/doc_network/howto.pdf deleted file mode 100644 index 83cbe3b27667b480d72af674a89599c3efeacf3b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 298764 zcma&uQ)4Z_wk_(|ww)QwI3u=g+qP}n#*A&-wr$(yUH6>xw)VrWr}_!)YrR*I%L|Lr zGSacYkk2g*ufebqF%a1qTEcL1!_Z5c*qS+;6EU%I0Eqt2fuR?*uy!_aB%&9!HgGl( zHZig@Hi6;gg>iCrG%>J&ao@<%kaF5;L+<%ho1)F8j*_~Y)>hQyGrZRPn{u3efX6@v z7z`GYg|pAIeI;7z|>u- zwYVBeu`0AH8HqbqZ0!SxDvW-l+P?N$(k?CsV)ps8U@vXI# zC?J0sI``UEw@kwtPH-!GxYCGz$EQ#Bu+f}6>hbzWJvcMbQ=x{bJpaPZ`;am{!NJlV zGNq^AnXFlUJ_QOF+iRA`n*2ck!IUT&piyOz1cCdSbK#|%d;=qSYq$M$&9;YhTinm} z<7>W*sh_&f&j1!2C>Z=hSs+K?%~&{}8i%T5XHj6oHG#*ZqR+DbBXT(`(cV@z!2Q9P z{8dOnh8cYATF{>X@({4=fc1IMQYbWv8&b15;w%T_6s$BZg$MDkOPrisGVGL|C91h z9Mv(BM6m8W_vJM6)`jigt~FBkOv*$7gRGP)OlgznGKblS(mYpE8;l$knW!laXfw!0 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b/doc_network/howto.tex deleted file mode 100644 index 780c457..0000000 --- a/doc_network/howto.tex +++ /dev/null @@ -1,600 +0,0 @@ -% Options for packages loaded elsewhere -\PassOptionsToPackage{unicode}{hyperref} -\PassOptionsToPackage{hyphens}{url} -% -\documentclass[ -]{article} -\usepackage{amsmath,amssymb} -\usepackage{lmodern} -\usepackage{ifxetex,ifluatex} -\ifnum 0\ifxetex 1\fi\ifluatex 1\fi=0 % if pdftex - \usepackage[T1]{fontenc} - \usepackage[utf8]{inputenc} - \usepackage{textcomp} % provide euro and other symbols -\else % if luatex or xetex - \usepackage{unicode-math} - \defaultfontfeatures{Scale=MatchLowercase} - \defaultfontfeatures[\rmfamily]{Ligatures=TeX,Scale=1} -\fi -% Use upquote if available, for straight quotes in verbatim environments -\IfFileExists{upquote.sty}{\usepackage{upquote}}{} -\IfFileExists{microtype.sty}{% use microtype if available - \usepackage[]{microtype} - \UseMicrotypeSet[protrusion]{basicmath} % disable protrusion for tt fonts -}{} -\makeatletter -\@ifundefined{KOMAClassName}{% if non-KOMA class - \IfFileExists{parskip.sty}{% - \usepackage{parskip} - }{% else - \setlength{\parindent}{0pt} - \setlength{\parskip}{6pt plus 2pt minus 1pt}} -}{% if KOMA class - \KOMAoptions{parskip=half}} -\makeatother -\usepackage{xcolor} -\IfFileExists{xurl.sty}{\usepackage{xurl}}{} % add URL line breaks if available -\IfFileExists{bookmark.sty}{\usepackage{bookmark}}{\usepackage{hyperref}} -\hypersetup{ - pdftitle={How to build an Open Science Monitor based on publications? A French perspective}, - pdfkeywords={open access, open science, open data, open source}, - hidelinks, - pdfcreator={LaTeX via pandoc}} -\urlstyle{same} % disable monospaced font for URLs -\usepackage[left=3cm, right=3cm, top=3cm, bottom=3cm]{geometry} -\setlength{\emergencystretch}{3em} % prevent overfull lines -\providecommand{\tightlist}{% - \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}} -\setcounter{secnumdepth}{-\maxdimen} % remove section numbering -\ifluatex - \usepackage{selnolig} % disable illegal ligatures -\fi -\newlength{\cslhangindent} -\setlength{\cslhangindent}{1.5em} -\newlength{\csllabelwidth} -\setlength{\csllabelwidth}{3em} -\newenvironment{CSLReferences}[2] % #1 hanging-ident, #2 entry spacing - {% don't indent paragraphs - \setlength{\parindent}{0pt} - % turn on hanging indent if param 1 is 1 - \ifodd #1 \everypar{\setlength{\hangindent}{\cslhangindent}}\ignorespaces\fi - % set entry spacing - \ifnum #2 > 0 - \setlength{\parskip}{#2\baselineskip} - \fi - }% - {} -\usepackage{calc} -\newcommand{\CSLBlock}[1]{#1\hfill\break} -\newcommand{\CSLLeftMargin}[1]{\parbox[t]{\csllabelwidth}{#1}} -\newcommand{\CSLRightInline}[1]{\parbox[t]{\linewidth - \csllabelwidth}{#1}\break} -\newcommand{\CSLIndent}[1]{\hspace{\cslhangindent}#1} -% for compatibility with pandoc 2.10 -\newenvironment{cslreferences}% - {\setlength{\parindent}{0pt}% - \everypar{\setlength{\hangindent}{\cslhangindent}}\ignorespaces}% - {\par} - -\title{How to build an Open Science Monitor based on publications? A -French perspective} -\usepackage{authblk} -\author[% - 2% - ]{% - Laetitia Bracco% - % - % -} -\author[% - 1% - ]{% - Eric Jeangirard% - % - % -} -\author[% - 1% - ]{% - Anne L'Hôte% - % - % -} -\author[% - 3% - ]{% - Laurent Romary% - % - % -} -\affil[1]{French Ministry of Higher Education and Research, Paris, -France} -\affil[2]{University of Lorraine, France} -\affil[3]{INRIA, France} -\date{December 2024} - -\makeatletter -\def\@maketitle{% - \newpage \null \vskip 2em - \begin {center}% - \let \footnote \thanks - {\LARGE \@title \par}% - \vskip 1.5em% - {\large \lineskip .5em% - \begin {tabular}[t]{c}% - \@author - \end {tabular}\par}% - \vskip 1em{\large \@date}% - \end {center}% - \par - \vskip 1.5em} -\makeatother - -\begin{document} -\maketitle -\begin{abstract} -Many countries and institutions are striving to develop tools to monitor -their open science policies. Since 2018, with the launch of its National -Plan for Open Science, France has been progressively implementing a -monitoring framework for its public policy, relying exclusively on -reliable, open, and controlled data. - -Currently, this monitoring focuses on research outputs, particularly -publications, as well as theses and clinical trials. Publications serve -as a basis for analyzing other dimensions, including research data, -code, and software. The metadata associated with publications is -therefore particularly valuable, but the methodology for leveraging it -raises several challenges. - -Here, we briefly outline how we have used this metadata to construct the -French Open Science Monitor. -\end{abstract} - -\textbf{Keywords}: open access, open science, open data, open source - -\hypertarget{requirements}{% -\section{1. Requirements}\label{requirements}} - -\hypertarget{data-to-gather}{% -\subsection{1.1 Data to gather}\label{data-to-gather}} - -The starting point for these analyses is a corpus of publications. -Defining the appropriate target scope is essential to provide relevant -insights. Details about the metadata required for this corpus are -provided in Section 2. In summary, describing this corpus with a PID -(Persistent Identifier) and associated metadata is crucial. The default -primary PID should be the Crossref DOI. Other PIDs can be used, but the -methodology and code must be adapted accordingly, particularly for Open -Access (OA) status discovery. - -Additional metadata are also required. A normalized scientific field -classification enables the creation of KPIs by scientific domain. If -such metadata are unavailable, they can be inferred using machine -learning models. OpenAlex also provides computed metadata that can be -leveraged. Metadata such as publishers, repositories, journals, -affiliations, and publication types can support further analyses, but -they must be normalized to ensure that the insights derived are -meaningful. - -Depending on the context, the corpus can be extracted from CRIS systems, -global databases (such as OpenAlex), or even custom-built. The French -Open Science Monitor (OSM) opted for the latter approach, combining open -data from Crossref, PubMed, open repositories, web crawling, and -bottom-up data contributions from French institutions that wish to -develop their own OSM. For global data sources, country affiliation is -determined based on harvested (or crawled) raw affiliation strings using -the affiliation matcher detailed in (L'Hôte and Jeangirard 2021). The -code and a Docker image are available here: -\url{https://github.com/dataesr/affiliation-matcher}. - -For Open Access publication KPIs, an OA status discovery tool is -required. By default, Unpaywall provides information for Crossref DOIs. - -For indicators related to datasets and software, in addition to the -metadata corpus, the full texts of publications in PDF format are also -required. Text and Data Mining (TDM) techniques can then be applied to -compute the KPIs. - -\textbf{Warning}: To achieve the best possible results, it is essential -to download as many full-text publications as possible. In the European -context, this is feasible under the framework of the European directive -allowing text and data mining for research purposes\footnote{Directive - (EU) 2019/790 of the European Parliament and of the Council of 17 - April 2019 on copyright and related rights in the Digital Single - Market and amending Directives 96/9/EC and 2001/29/EC - \url{https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32019L0790}}. -It is necessary to have lawful access to the downloaded content if it is -not already openly accessible, for instance, via a subscription. Outside -the European Union, the legal framework must be carefully reviewed. This -note does not address the context beyond Europe. - -\hypertarget{open-source-software-used}{% -\subsection{1.2 Open source software -used}\label{open-source-software-used}} - -The French Monitor code is freely available under open license (MIT -License). It is modular as detailed in the infrastructure section in -(Bracco et al. 2022) (for OA to publications) and in (Bassinet et al. -2023) (for datasets and software). However, it is closely linked to our -data acquisition pipeline (web crawling, bottom data collection from -French institutions, extension to cover French specific PID (HAL)) and -to the data architecture we built on the OVH public cloud - S3 Object -Storage used by the Ministry. Also, Python has been used. If it is to be -implemented in another country, parts of the code will have to be -rewritten to match the local requirements. The core monitor code also -relies on other free and open source services / software: - -\begin{itemize} -\item - Open access discovery tool (as explained above): Unpaywall The premium - service of Unpaywall is used to get a quarterly full snapshot of the - database. These snapshots are used to historicize the OA status of - each publication, useful to analyse the OA dynamics. -\item - A Text and Data Mining (TDM) tool to detect research datasets mentions - from the full-text. We use - \href{https://github.com/kermitt2/datastet}{DataStet} from the - \href{https://hub.docker.com/r/grobid/datastet/tags}{Docker image - 0.8.0} -\item - A Text and Data Mining (TDM) tool to detect code and software mentions - from the full-text. We use - \href{https://github.com/softcite/software-mentions}{Softcite} from - the - \href{https://hub.docker.com/r/grobid/software-mentions/tags}{Docker - image 0.8.0} -\item - A smart scholarly PDF parsing tool to structure metadata and the - full-text content from a PDF. We use - \href{https://github.com/kermitt2/grobid}{GROBID} from the - \href{https://hub.docker.com/r/grobid/grobid/tags}{Docker image - 0.8.0}. Later versions (from 0.8.1) include fixes on grant ids - detection that can be very relevant. -\end{itemize} - -We also developed extra modules that glues together and orchestrates all -the previous tools: - -\begin{itemize} -\item - The module - \href{https://github.com/dataesr/bso3-analyse-publications/blob/main/application/server/main/tasks.py\#L99}{bso3-analyse-publications} - implements in Python the whole - \href{https://github.com/dataesr/bso3-analyse-publications/blob/main/application/server/main/tasks.py\#L99}{TDM - pipeline}, and also the analysis - \href{https://github.com/dataesr/bso3-analyse-publications/blob/main/application/server/main/tasks.py\#L176}{funnel - analysis} at the document level of the resulting outputs to get - publication-wise KPIs. -\item - The module - \href{https://github.com/dataesr/bso-publications}{bso-publications} - implements in python an extract-transform-load process and stores the - final results, at the publication level, in an Elasticsearch index. -\end{itemize} - -\hypertarget{computation-power-consumed}{% -\subsection{1.3 Computation power -consumed}\label{computation-power-consumed}} - -We deploy our code on a public cloud infrastructure, by OVH. We use -their managed Kubernetes service, using multiple nodes (servers): - -\begin{itemize} -\item - 1 large server (16 CPU 240Go RAM) is used to host the metadata Mongo - databases (Unpaywall snapshots, French corpus metadata) and run most - of the OA KPIs calculations. -\item - 6 smaller servers are used for data acquisition (crawling, parsing, - harvesting) and enrichment (language detection, discipline inference, - tasks monitoring \ldots) (4 CPU 15Go RAM and 2 CPU 7Go RAM). -\item - 1 medium server (4 CPU 60Go RAM) used to harvest the PDF. -\item - 5 servers (32 CPU 120Go RAM) to run the TDM analysis. -\item - 3 servers (with redundancy) (16 CPU 60Go RAM) to host the - Elasticsearch resulting indices. -\item - 6 servers (with redundancy + staging/prod) (2 CPU 7Go) to host the - website. -\end{itemize} - -This infrastructure is also used for other projects, so 100\% cannot be -affected to the OS monitoring (in particular the Elasticsearch and -websites hosting part). However, the PDF harvesting and TDM analysis are -a very specific need. For this specific need, around 20k euros were -spent to analyse 700k PDFs in one month. The rest of the infrastructure -is around 70k a year, but is not specific to OS monitoring. Relying on -other services (like OpenAlex) could help reduce the costs. - -\hypertarget{team-and-human-resources}{% -\subsection{1.4 Team and human -resources}\label{team-and-human-resources}} - -A deep understanding of the Open science / scholarly communications area -is key to make it happen. Random software engineers do not have this -knowledge and it may take time for them to understand what is at stake -for the monitoring to be relevant. Building the whole pipeline can be -implemented with about 2 FTE for 6 months. An extra FTE (project manager -or so) can be needed to make sure software developments are inline with -the project goals. Maintenance costs are lower, about 0.5 FTE a year. -However, things evoles fast and new features (new objects to monitor, -new types of analysis \ldots) are generally necessary so a maintenance -only scenario is not very likely to happen. - -\hypertarget{a-few-methodological-considerations}{% -\section{2. A few methodological -considerations}\label{a-few-methodological-considerations}} - -\hypertarget{corpus-creation}{% -\subsection{2.1 Corpus creation}\label{corpus-creation}} - -\hypertarget{defining-a-perimeter}{% -\subsubsection{2.1.1 Defining a perimeter}\label{defining-a-perimeter}} - -Research outputs can be indexed in large databases (Crossref, Datacite, -OpenAlex), or not. It can be necessary to put in place specific -harvesters to get metadata from other places (disciplinary or -institutional repositories for example). - -Making sure these extra data are correctly ingested with the data coming -from the large database is a challenge (data format, no duplicates -\ldots). An option could be to make those extra research outputs fit -into one of these databases: adding DOI for example, or asking OpenAlex -to harvest extra repositories. - -Also, about the distinction between types of publication, for example, -the distinction with professional articles: it all depends on the -available data, of course, but also the main point to define the -perimeter is to know upfront what is the goal of the monitoring. If it -is to steer and analyze the impact of a public policy, then the -perimeter has to be in line with the public policy itself. That may be -different from one national/institutional situation to another. That is -also why, in France, we propose a national monitoring, but also -``local'' monitoring in which the perimeter can be customed by the -users. - -\hypertarget{handling-duplicates}{% -\subsubsection{2.1.2 Handling duplicates}\label{handling-duplicates}} - -Detecting duplicates can be challenging. For publications with a -Crossref DOI, this PID is in general enough, even though, for some -cases, preprints and published versions sometimes have a Crossref DOI -each and could be considered as only one research output. - -For works from Datacite, that can be much more complicated. The Datacite -API has a field ``relatedIdentifiers'' that can be used to detect -duplicates. - -In the example from -\href{https://openalex.org/works?page=1\&filter=authorships.author.id\%3Aa5044430271,display_name.search\%3Aconverging}{OpenAlex} -one can see from the datacite API -https://api.datacite.org/dois/10.5281/zenodo.8042997 that this DOI has -several versions. However that may not be enough and some heuristics -based on title, authors \ldots{} or even more complicated processes -(machine learning etc) could be used to detect duplicates. The same goes -for publications from other systems (open repositories for example) that -may contain a lot of duplicates. - -Also, during the deduplication phase, duplicate records may have to be -merged into one single record. The list of affiliations, keywords, and -authors can be different. Merging strategies have to be settled. In the -French case, for key elements, Crossref data have the top priority, -followed by the other sources (web scraping, PubMed, HAL, \ldots). For -some metadata that are of type list, it is possible to enrich the final -metadata with all the metadata coming from each source (affiliations, -keywords for example). - -\hypertarget{author-disambiguation-and-affiliations}{% -\subsubsection{2.1.3 Author disambiguation and -affiliations}\label{author-disambiguation-and-affiliations}} - -Grobid can help to get authors' PID (like ORCID) or raw affiliation -strings and PID (like ROR) when they are present in the full text. -However other sources have to be considered to get better coverage. - -Affiliations can be harvested from the landing pages in many cases (from -HTML Highwire header or from HTML parsing). That is one the techniques -used by OpenAlex. Aligning those raw affiliation strings to ROR is -another step where heuristics or machine learning can be helpful (see -(L'Hôte and Jeangirard 2021)). The -\href{https://works-magnet.esr.gouv.fr/}{Works-magnet -https://works-magnet.esr.gouv.fr/} (see (Jeangirard, Bracco, and L'Hôte -2024)) is a tool designed to help improve this matching done -automatically in OpenAlex. Author disambiguation is not an easy task -either as the majority of authors have no ORCID at all. OpenAlex uses -clustering techniques -https://github.com/ourresearch/openalex-name-disambiguation/tree/main/V3 - -In the French case, we benefit from a large registry of persons -(https://www.idref.fr/) maintained by ABES. This registry has quite a -good coverage of the researchers working in France. Again, clustering -techniques can be used to disambiguate author name with a PID, but a -good person registry helps. These techniques are used in the French -research portal scanR (https://scanr.enseignementsup-recherche.gouv.fr/) -to add PID (namely idref ID) to the authors of publications. - -\hypertarget{corpus-enrichment}{% -\subsection{2.2 Corpus enrichment}\label{corpus-enrichment}} - -Once the publication corpus is defined, many enrichment are necessary to -add dimensions to analyse. The use of machine-learning techniques can -help a lot, but additional care is required to verify the results (see -(Jeangirard 2022)). - -\hypertarget{discipline-classification}{% -\subsubsection{2.2.1 Discipline -classification}\label{discipline-classification}} - -Different algorithms based on title, ISSN, authors etc \ldots{} can be -used to classify publications. OpenAlex implements some. The French Open -Science Monitoring uses its own to classify publications into 10 -macro-categories (cf (Jeangirard 2019)). - -\hypertarget{open-access-discovery}{% -\subsubsection{2.2.2 Open Access -discovery}\label{open-access-discovery}} - -The Open Access status information is generally not retrieved with -Grobid (publication parser), but instead using information from the -landing page (HTML parser) or from repositories. Unpaywall (and OpenAlex -now) already implements that logic, at least for all Crossref DOI. For -other publications (with no Crossref DOI), open access discovery is not -that easy and depends on local specificities. In the future, we could -expect that, if those publications are in OpenAlex, they also benefit -from a better open access discovery service just like with Unpaywall. -Also, it is important to note that the open access status is not a fixed -metadata (contrary to the title or the list of authors for instance). It -can evolve over time. More details on this aspect are given in (Bracco -et al. 2022). - -\hypertarget{open-access-types}{% -\subsubsection{2.2.3 Open Access types}\label{open-access-types}} - -In the French Open Science Monitor, we analyze in different ways the -type of open access. In particular, - -\begin{itemize} -\item - is it opened via the publisher or via a repository (or both?) - - regardless of the license -\item - if it is opened via the publisher, what kind of business model is it - used? -\item - if it is opened via the publisher, is there any proper license, and - which one? -\end{itemize} - -More details are described in (Bracco et al. 2022). - -\hypertarget{apc-estimation}{% -\subsubsection{2.2.4 APC estimation}\label{apc-estimation}} - -Estimating Article Processing Charge (APC) is not an easy task. In the -French OSM, we implemented an article level estimation, based on the -data from DOAJ and OpenAPC (cf (Bracco et al. 2022)). In particular, if -an article, not present in OpenAPC, is published in a journal that is -sufficiently represented in OpenAPC (for the same year of publication), -an APC amount is estimated based on the average APC paid for articles -published in that journal in the same year. OpenAlex provides also some -insights, based in particular on listed APC. However, two main -difficulties are difficult to overcome. First, when an article is in -collaboration, it is very difficult to know which institution has -actually paid. The institution of the corresponding author is probably a -good guess, but the corresponding author metadata is hard to get. -Another difficulty comes from the transformative agreements, where APC -are negotiated at a larger scale. The marginal APC per article is then -harder to estimate. - -\hypertarget{dataset-and-software-mention-detection}{% -\subsubsection{2.3 Dataset and software mention -detection}\label{dataset-and-software-mention-detection}} - -Research datasets, software and code are research outputs difficult to -monitor. We explored a very generic technique to analyse those objects: -this technique can be applied to any country and discipline, as long as -a publication corpus is available. Publications' full-texts are analyzed -with deep learning models to detect all the mentions of data / datasets, -and also all the mentions of software and code. Those mentions are then -caracterized in context, that means they are classified according to the -type of mention. In the model we use, each mention can be a mention of -usage, creation, or sharing. Once all the mentions detected are -caracterized, document-level indicators are computed, to calculate -whether a publication does use / create / share data or software. -Eventually, the French OSM computes national-level indiators, with the -percentage of publication that mentions sharing data (/ software) -amongst the publications that indicates using and producing data (/ -software), see (Bassinet et al. 2023). - -The detection models (Softcite and Datastet) can be improved for sure. -The \href{https://works-magnet.esr.gouv.fr/}{works-magnet -https://works-magnet.esr.gouv.fr/} provides a module to explore and -correct the mentions detected in the French corpus. We hope to collect -enough good quality curated data to build an extended training dataset -and then more accurate machine learning models for dataset and software -detection. We are convinced setting in place quick human feedback loop -with increase the accuracy of the detection models. Works-magnet like -tools enable that kind of interactions and could be integrated in -different pipelines, like on manuscript deposit platforms. - -\hypertarget{a-few-advices-and-impact}{% -\section{3. A few advices and impact}\label{a-few-advices-and-impact}} - -Having an objective and quantitative tool for monitoring, easy to plug, -which makes it easier for funders and institutions to communicate and -steer their open science policy. Having a tool that can be easily -adapted to different contexts like funders or institutions is then a key -component. However, a tool remains a tool and is only complementary to -the local policies and mandates. - -Some tips also can help in building a reliable and effective Open -Science Monitoring. - -\begin{itemize} -\item - Clean up OpenAlex affiliations to obtain a reliable corpus of - publications (the - \href{https://works-magnet.dataesr.ovh/}{works-magnet} can help). -\item - Maintaining its own infrastructure is costly (money and HR). A call to - open infrastructure could help reduce the costs and invest in a shared - infrastructure / methodology. -\item - If a national dashboard cannot be created, it is already possible to - obtain useful indicators on open access publications directly via COKI - (see (Diprose 2023)), as it is based on OpenAlex data. -\item - In the event of legal and/or economic difficulties in accessing - non-open access content, a downgraded version of the result based - solely on open access full text and under CC licence is possible. -\end{itemize} - -\hypertarget{references}{% -\section*{References}\label{references}} -\addcontentsline{toc}{section}{References} - -\hypertarget{refs}{} -\begin{cslreferences} -\leavevmode\hypertarget{ref-bassinet:hal-04121339}{}% -Bassinet, Aricia, Laetitia Bracco, Anne L'Hôte, Eric Jeangirard, Patrice -Lopez, and Laurent Romary. 2023. ``Large-scale Machine-Learning analysis -of scientific PDF for monitoring the production and the openness of -research data and software in France.'' -\url{https://hal.science/hal-04121339}. - -\leavevmode\hypertarget{ref-bracco:hal-03651518}{}% -Bracco, Laetitia, Anne L'Hôte, Eric Jeangirard, and Didier Torny. 2022. -``Extending the open monitoring of open science.'' -\url{https://hal.science/hal-03651518}. - -\leavevmode\hypertarget{ref-coki}{}% -Diprose, Hosking, J. P. 2023. ``User-Friendly Dashboard for Tracking -Global Open Access Performance.'' \emph{The Journal of Electronic -Publishing}. \url{https://doi.org/10.3998/jep.3398}. - -\leavevmode\hypertarget{ref-jeangirard_monitoring_2019}{}% -Jeangirard, E. 2019. ``Monitoring Open Access at a National Level: -French Case Study.'' In \emph{ELPUB 2019 23d International Conference on -Electronic Publishing}. OpenEdition Press. -\url{https://doi.org/10.4000/proceedings.elpub.2019.20}. - -\leavevmode\hypertarget{ref-jeangirard:hal-04598201}{}% -Jeangirard, Eric, Laetitia Bracco, and Anne L'Hôte. 2024. ``Works-magnet -: aucune de perdue, 10 000 de retrouvées.'' Abes; Journées Abes 2024. -\url{https://doi.org/10.5281/zenodo.11471247}. - -\leavevmode\hypertarget{ref-jeangirard:hal-03819060}{}% -Jeangirard, Éric. 2022. ``L'utilisation de l'apprentissage automatique -dans le Baromètre de la science ouverte~: une façon de réconcilier -bibliométrie et science ouverte~?'' \emph{Arabesques}, no. 107 -(September): 10--11. \url{https://doi.org/10.35562/arabesques.3084}. - -\leavevmode\hypertarget{ref-lhote:hal-03365806}{}% -L'Hôte, Anne, and Eric Jeangirard. 2021. ``Using Elasticsearch for -entity recognition in affiliation disambiguation.'' -\url{https://hal.science/hal-03365806}. -\end{cslreferences} - - -\end{document} diff --git a/doc_network/mapping_at_scale.pdf b/doc_network/mapping_at_scale.pdf index 2a4d5a9fd7a64ee1c92d1ef3c9f09ce218fd0309..5e29da4457aaff5fe7d876dd7ff56db995451268 100644 GIT binary patch delta 53997 zcmZs?V~{Su)}-6EZQJ&4+r8ViZTH)@ZQHhO?zU~)bG|zeX~aap)Cmd*fb;=l|E;UxSufNyU29baDFjj@NqKCrc2GgRpU>-6emJJotjLOC>zSb-N 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zIq?rNW~wD5D8j$^XFpVF)c>7+;qUYq|AqY>iKQk%f|CCW^j|02{{~T~F8v3z{eKI5 z{~N@ViVFow{tr=MsJ}$1u2T)5K*|0^biZR>xZv;d{r{ikm7Y@b|AYE(Zt%aSrg%x+ OhXSR7D*O6}`~L%!Fs7ye diff --git a/doc_network/out.html b/doc_network/out.html index 8633392..c43b966 100644 --- a/doc_network/out.html +++ b/doc_network/out.html @@ -512,7 +512,7 @@

Mapping scientific communities at scale

1. Motivation

Analysing and mapping scientific communities provides an insight into the structure and evolution of academic disciplines. This involves providing an analytical and visual representation of the relationships between entities (e.g. researchers, research laboratories, research themes), with the aim, in particular, of understanding the networks and dynamics of scientific collaboration, and identifying collaborative groups and their influences. From the point of view of decision-makers, this type of tool is useful for strategic decision-making with a view to public policy and funding.

These maps are generally deduced from data in bibliographic databases (open or proprietary), based on co-publication or citation information. In the case of co-publications, two entities (authors, for example) will be linked if they have collaborated (co-published) on a piece of research. These links are then symmetrical. In the case of citation links, two authors will be linked if one cites the research work of another, in the list of references. This is a directed link, as one author may cite another without this being reciprocal. A lot of recent work uses this second approach, for example by trying to calculate composite indicators of novelty (or innovation) based on citation links.

-

The quality and completeness of the bibliographic metadata used are, of course, essential if we are to produce a relevant map. Today, the quality of open citation data still needs to be improved, cf (Alperin et al. 2024). On the other hand, it is possible to obtain quality metadata on publications (and therefore links to co-publications). For example, the French Open Science Monitor (BSO) has compiled a corpus of French publications with good coverage cf (Chaignon and Egret 2022). This corpus is exposed in the French research portal scanR (Jeangirard 2024). This is a corpus containing about 4 millions publications in all disciplines. These publications have been enriched with disambuation persistent identifier (PID) on authors, affiliations and topics. For authors and affiliations, French-specific PID have been used (idref for authors and RNSR for laboratories) because they have the best coverage, even if not perfect. For topics, wikidata identifiers has been used cf (Foppiano and Romary 2020). Other enrichments, like software detection are also present, and thus usable as entities to map.

+

The quality and completeness of the bibliographic metadata used are, of course, essential if we are to produce a relevant map. Today, the quality of open citation data still needs to be improved, cf (Alperin et al. 2024). On the other hand, it is possible to obtain quality metadata on publications (and therefore links to co-publications). For example, the French Open Science Monitor (BSO) has compiled a corpus of French publications with good coverage cf (Chaignon and Egret 2022). This corpus is exposed in the French research portal scanR (Jeangirard 2024). This is a corpus containing about 4 millions publications in all disciplines. These publications have been enriched with disambuation persistent identifier (PID) on authors, affiliations and topics.

1.1 Previous limits of the scanR application

Launched in 2016, the scanR portal used to be a search engine. Its scope first focused on research entities (institutions, laboratories and private companies) and was extended in 2020 to cover fundings, publications, patents and authors. Two main use cases were covered. Firstly, the ability to generate a list of search results corresponding to a user query. A list of laboratories, authors, funding or publications could be generated. Secondly, for each institution (or laboratory), a unified view of all the data concerning it was grouped together on a dedicated page in scanR (administrative information, list of publications, list of funding, main partners, etc.).

However, these functions only gave a flat view of the different dimensions, without providing any insights into the interactions between laboratories or authors. For a user interested in a research theme, for example, the list of the main contributors (those who have co-authored the most publications) does not give a clear idea of which research communities are at work and how they interact with each other. A network analysis tool to describe these interactions and attempt to detect research communities could therefore enable us to go further in creating tools to help explore fields of research and innovation.

@@ -523,7 +523,13 @@

2. Network analysis at scale

2.1 Focusing on strongest interactions

One of the added values of mapping with a network view is to show the interactions between entities, i.e. the links between the nodes in the graph. These links provide crucial information that can be used to structure communities. If the size of the network needs to be reduced (for reasons of computation, speed, legibility and interpretability), it is vital to preserve the links that carry the most information, i.e. the strongest interactions. With this reasoning, it seems logical to reduce the size of the network by only affecting the strongest links.

Thus, from a given corpus, however large, we seek to extract the pairs of entities with the strongest interactions, for example the most co-signatures per pair of authors. From this list of pairs, we can naturally find the nodes of the graph and deduce a new graph. If the graph has several independent components, i.e. several unconnected sub-graphs, we can decide to keep only the main component(s).

-

2.2 Elasticsearch implementation

+

2.2 Publication metadata enrichment to produce different mapping

+

Each publication in the scanR corpus goes through a systematic enrichment pipeline, including author and affiliation disambiguation, full-text parsing, topic detection.

+

For authors, the French-specific persistent identifier (PID) https://www.idref.fr is used. Its coverage, even if not perfect, for French affiliated authors is strong thanks to the deep linking between idref and the PhD thesis registration in France. Specific heuristics have been implemented to disambiguate names and link them to idref.

+

For affiliations, again French specific PID are used, especially SIRENE and RNSR. A specific module based on Elasticsearch https://github.com/dataesr/affiliation-matcher has been implemented to automatically link pblications to those PIDs (L’Hôte and Jeangirard 2021).

+

For topics, wikidata identifiers has been used using the entity-fishing module https://github.com/kermitt2/entity-fishing cf (Foppiano and Romary 2020).

+

Other enrichments, like software detection are also present. These are based on software mentions detections using GROBID and Softcite at scale on the French corpus (Bassinet et al. 2023).

+

2.3 Elasticsearch implementation

To identify the strongest links, it would be too costly to go through the entire corpus. We have pre-calculated the links at the level of each publication. So, if a publication is linked to 3 themes, T1, T2 and T3, a pre-calculated field, at publication level, contains all T1-T2, T1-T3 and T2-T3 pairs. This co_topics field represents the co-appearance links within the publication. We then use elasticsearch’s aggregation functionality to list the most present links, very efficiently.

In practice, a PID is also stored (the wikidata for topics, for example) to disambiguate entities. In practice, for a given query, elasticsearch returns a response containing the strongest links, for example:

                {
@@ -558,7 +564,7 @@ 
2.2 Elasticsearch implementation

                     "key": "Q15305550###Carbon sequestration---Q623###carbon",
                     "doc_count": 7,
                 },
-

2.3 Network creation

+

2.4 Network creation

The network creation process involves several key steps: transforming Elasticsearch results into a graph using Graphology, filtering the network to focus on the most interesting nodes, applying spatialization algorithms for visualization, and detecting communities within the network. Below, we detail each of these steps.

The network creation process begins with the results obtained from Elasticsearch, utilizing the open-source JavaScript library Graphology https://github.com/graphology/graphology to construct and manipulate the network. Each link result from Elasticsearch is transformed into nodes and edges, with the edge strength corresponding to the interaction intensity derived from the Elasticsearch aggregations.

To ensure that the network remains manageable and focuses on the most interesting nodes, we employ a strategy that prioritizes the best-connected nodes rather than the largest nodes. By default, the maximum number of nodes is set to 300. This threshold helps in maintaining the computational efficiency and interpretability of the network.

@@ -572,7 +578,8 @@

2.3 Network creation

slowDown: 1 + Math.log(order)

In graph theory, a community corresponds to a set of nodes in a graph that are strongly interconnected with each other, while being less connected with nodes outside this community. Communities can be identified in order to understand the underlying structure and patterns of the graph, as well as to analyze the relationships and interactions between the entities that make it up. To identify and visualize communities within the network, we apply the Louvain algorithm using Graphology. This algorithm works by optimizing a modularity measure that evaluates the strength of communities in a graph (Blondel et al. 2008). More precisely, Louvain seeks to maximize modularity by progressively moving the nodes of a graph into different communities, in an iterative fashion. At each stage, he merges neighboring communities if this leads to an improvement in the overall modularity of the graph. This iterative process continues until no further moves can increase modularity.
This step helps in revealing the underlying structure and communities within the scientific network, providing valuable insights into the interactions and collaborations within the bibliometric data.

-

2.4 VOSviewer implementation

+

The graphology-communities-louvain node module is being used. This way, each step (like spatizalization, community-detection) are implemented modularly. A benchmark, in our use case, of the Louvain and the Leiden algorithms would be desirable. The graphology library started a while ago working on an implementation of the leiden algorithm (see https://github.com/graphology/graphology/tree/master/src/communities-leiden) but that remains to be implemented.

+

2.5 VOSviewer implementation

To display the network within our application, we use the open source VOSviewer online tool for network visualization https://github.com/neesjanvaneck/VOSviewer-Online. It is based on the VOSviewer software which is very popular for network analysis in bibliometric studies (Waltman, Eck, and Noyons 2010).

VOSviewer accepts JSON files formatted according to a specific template https://app.vosviewer.com/docs/file-types/json-file-type. This template includes essential attributes for nodes and edges, such as the node ID, name, position, and additional metadata. To ensure compatibility, we transform our Graphology object into a JSON file that adheres to VOSviewer’s required format.

Once the JSON file is generated, VOSviewer renders the network, displaying nodes and edges in an interactive and visually appealing manner. The nodes are colorized based on the communities identified through the clustering process performed using the Louvain algorithm. This colorization helps in visually distinguishing different communities within the network, making it easier to analyze and interpret the underlying structure and interactions.

@@ -600,6 +607,9 @@

References

Alperin, Juan Pablo, Jason Portenoy, Kyle Demes, Vincent Larivière, and Stefanie Haustein. 2024. “An Analysis of the Suitability of Openalex for Bibliometric Analyses.” https://arxiv.org/abs/2404.17663.

+
+

Bassinet, Aricia, Laetitia Bracco, Anne L’Hôte, Eric Jeangirard, Patrice Lopez, and Laurent Romary. 2023. “Large-scale Machine-Learning analysis of scientific PDF for monitoring the production and the openness of research data and software in France.” https://hal.science/hal-04121339.

+

Blondel, Vincent D, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. “Fast Unfolding of Communities in Large Networks.” Journal of Statistical Mechanics: Theory and Experiment 2008 (10): P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008.

diff --git a/doc_network/out.latex b/doc_network/out.latex index d471484..1d68636 100644 --- a/doc_network/out.latex +++ b/doc_network/out.latex @@ -203,12 +203,7 @@ publications with good coverage cf (Chaignon and Egret 2022). This corpus is exposed in the French research portal scanR (Jeangirard 2024). This is a corpus containing about 4 millions publications in all disciplines. These publications have been enriched with disambuation -persistent identifier (PID) on authors, affiliations and topics. For -authors and affiliations, French-specific PID have been used (idref for -authors and RNSR for laboratories) because they have the best coverage, -even if not perfect. For topics, wikidata identifiers has been used cf -(Foppiano and Romary 2020). Other enrichments, like software detection -are also present, and thus usable as entities to map. +persistent identifier (PID) on authors, affiliations and topics. \hypertarget{previous-limits-of-the-scanr-application}{% \subsection{1.1 Previous limits of the scanR @@ -291,8 +286,37 @@ naturally find the nodes of the graph and deduce a new graph. If the graph has several independent components, i.e.~several unconnected sub-graphs, we can decide to keep only the main component(s). +\hypertarget{publication-metadata-enrichment-to-produce-different-mapping}{% +\subsection{2.2 Publication metadata enrichment to produce different +mapping}\label{publication-metadata-enrichment-to-produce-different-mapping}} + +Each publication in the scanR corpus goes through a systematic +enrichment pipeline, including author and affiliation disambiguation, +full-text parsing, topic detection. + +For authors, the French-specific persistent identifier (PID) +\url{https://www.idref.fr} is used. Its coverage, even if not perfect, +for French affiliated authors is strong thanks to the deep linking +between idref and the PhD thesis registration in France. Specific +heuristics have been implemented to disambiguate names and link them to +idref. + +For affiliations, again French specific PID are used, especially SIRENE +and RNSR. A specific module based on Elasticsearch +\url{https://github.com/dataesr/affiliation-matcher} has been +implemented to automatically link pblications to those PIDs (L'Hôte and +Jeangirard 2021). + +For topics, wikidata identifiers has been used using the entity-fishing +module \url{https://github.com/kermitt2/entity-fishing} cf (Foppiano and +Romary 2020). + +Other enrichments, like software detection are also present. These are +based on software mentions detections using GROBID and Softcite at scale +on the French corpus (Bassinet et al. 2023). + \hypertarget{elasticsearch-implementation}{% -\subsection{2.2 Elasticsearch +\subsection{2.3 Elasticsearch implementation}\label{elasticsearch-implementation}} To identify the strongest links, it would be too costly to go through @@ -345,7 +369,7 @@ returns a response containing the strongest links, for example: \end{Shaded} \hypertarget{network-creation}{% -\subsection{2.3 Network creation}\label{network-creation}} +\subsection{2.4 Network creation}\label{network-creation}} The network creation process involves several key steps: transforming Elasticsearch results into a graph using Graphology, filtering the @@ -416,8 +440,16 @@ This step helps in revealing the underlying structure and communities within the scientific network, providing valuable insights into the interactions and collaborations within the bibliometric data. +The \texttt{graphology-communities-louvain} node module is being used. +This way, each step (like spatizalization, community-detection) are +implemented modularly. A benchmark, in our use case, of the Louvain and +the Leiden algorithms would be desirable. The graphology library started +a while ago working on an implementation of the leiden algorithm (see +\url{https://github.com/graphology/graphology/tree/master/src/communities-leiden}) +but that remains to be implemented. + \hypertarget{vosviewer-implementation}{% -\subsection{2.4 VOSviewer +\subsection{2.5 VOSviewer implementation}\label{vosviewer-implementation}} To display the network within our application, we use the open source @@ -532,6 +564,13 @@ Alperin, Juan Pablo, Jason Portenoy, Kyle Demes, Vincent Larivière, and Stefanie Haustein. 2024. ``An Analysis of the Suitability of Openalex for Bibliometric Analyses.'' \url{https://arxiv.org/abs/2404.17663}. +\leavevmode\hypertarget{ref-bassinet:hal-04121339}{}% +Bassinet, Aricia, Laetitia Bracco, Anne L'Hôte, Eric Jeangirard, Patrice +Lopez, and Laurent Romary. 2023. ``Large-scale Machine-Learning analysis +of scientific PDF for monitoring the production and the openness of +research data and software in France.'' +\url{https://hal.science/hal-04121339}. + \leavevmode\hypertarget{ref-Blondel_2008}{}% Blondel, Vincent D, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. ``Fast Unfolding of Communities in Large Networks.'' diff --git a/doc_network/out.odt b/doc_network/out.odt index ff803921209715ec670c61d64d01fd74df56c183..bdc013eb18f32609a4c09c7a18093c8723b9d5fc 100644 GIT binary patch delta 10997 zcmZv?WlY^&@GX3Bm*Vd3PVoc9-3t^b?(Y7>Def-C-K~@&2X|?4cXx{G?epfnx%uDR z{beP4ubHe5vop!eDvXA0NPtCERe*uT0YM{zK%ap`R0X8Z0GnP=-_ZbHq7fyKRNdZy zrCr&ao?5AG-x6WSWUBIw*pzxDbYf#696VA7bw4m6ec=%dwUyPv%aA_1$#7jVnnbzRBmF_Cre5+l5sP!oA^fb4 z#J<%ZY_vp6##v+Hvs_!mL4yULWQ}XCwD-}cbI{)-)P-*=Ykrt)fDj%jsApSv;_4e3 z@&*%=(Lqx4JhoaOMxk%JR9ektJ^Am{E&VrJP`rUjU4md137i`_l^Ypg@j%7ofJuag zF5kN@Lh&9-IhU{FlIQqKOkS5C27F*7 zJ56ksGAEZ&5K*g4EMMFxfrzY4uF)qRY2+M7FR-SX_w|9dhtY1PS~LeuDiZhfm%lU_ zK!M6^3bZ2GGaiWu!PGl|sHxSf+9&hr>%8*cH_Im70YzQ%Yd*R+X>h|Z-VeDn`p09L 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